Your browser doesn't support javascript.
loading
Identification of cancer stem cell characteristics in liver hepatocellular carcinoma by WGCNA analysis of transcriptome stemness index.
Bai, Kun-Hao; He, Si-Yuan; Shu, Ling-Ling; Wang, Wei-Da; Lin, Shi-Yong; Zhang, Qian-Yi; Li, Liang; Cheng, Lei; Dai, Yu-Jun.
Afiliação
  • Bai KH; Department of Endoscopy, Sun Yat-Sen University Cancer Center, Guangzhou, China.
  • He SY; State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Shu LL; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Wang WD; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.
  • Lin SY; State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Zhang QY; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Li L; Department of Hematological Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
  • Cheng L; State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Dai YJ; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Cancer Med ; 9(12): 4290-4298, 2020 06.
Article em En | MEDLINE | ID: mdl-32311840
ABSTRACT
Cancer stem cells (CSCs) are characterized by self-renewal and -differential potential as compared to common cancer cells and play an important role in the development and therapeutic resistance of liver hepatocellular carcinoma (LIHC). However, the specific pathogenesis of LIHC stem cells is still unclear, and the genes involved in the stemness of LIHC stem cells are currently unknown. In this study, we investigated novel biomarkers associated with LIHC and explored the expression characteristics of stem cell-related genes in LIHC. We found that mRNA expression-based stemness index (mRNAsi) was significantly overexpressed in liver cancer tissues. Further, mRNAsi expression in LIHC increased with the tumor pathological grade, with grade 4 tumors harboring the greatest stem cell features. Upon establishing mRNAsi scores based on mRNA expression of every gene, we found an association with poor overall survival in LIHC. Moreover, modules of interest were determined based on weighted gene co-expression network analysis (WGCNA) inclusion criteria, and three significant modules (red, green, and brown) and 21 key genes (DCN, ECM1, HAND2, PTGIS, SFRP1, SRPX, COLEC10, GRP182, ADAMTS7, CD200, CDH11, COL8A1, FAP, LZTS1, MAP1B, NAV1, NOTCH3, OLFML2A, PRR16, TMEM119, and VCAN) were identified. Functional analysis of these 21 genes demonstrated their enrichment in pathways involved in angiogenesis, negative regulation of DNA-binding transcription factor activity, apoptosis, and autophagy. Causal relationship with proteins indicated that the Wnt, Notch, and Hypoxia pathways are closely related to LIHC tumorigenesis. To our knowledge, this is the first report of a novel CSC biomarker, mRNAsi, to predict the prognosis of LIHC. Further, we identified 21 key genes through mRNA expression network analysis, which could be potential therapeutic targets to inhibit the stemness of cancer cells in LIHC.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Células-Tronco Neoplásicas / Biomarcadores Tumorais / Regulação Neoplásica da Expressão Gênica / Carcinoma Hepatocelular / Transcriptoma / Neoplasias Hepáticas Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Células-Tronco Neoplásicas / Biomarcadores Tumorais / Regulação Neoplásica da Expressão Gênica / Carcinoma Hepatocelular / Transcriptoma / Neoplasias Hepáticas Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article